Abstract
As people in different rooms usually have different thermal comfort feelings or demands, it is valuable to study the modeling and control of thermal comfort to meet the personalized requirements. This paper tries to solve this issue using the data collected by the temperature and humidity sensors in the working or living time periods in the room being studied. We firstly present a statistic method based sensor data preprocessing strategy to discard noisy data and obtain the reasonable intervals for the temperature and humidity of each day. Then, we construct the Gaussian interval type-2 fuzzy set models to depict the personalized temperature and humidity comfort through measuring the uncertainty degrees of the obtained intervals. At last, we propose a control scheme to realize the personalized thermal comfort regulation. Our results show that the constructed thermal comfort models can recommend a reasonable temperature and humidity range for the demand in a specific room.
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Acknowledgments
This work is supported by National Natural Science Foundation of China (61473176, 61402260, and 61273149), the Open Program from the State Key Laboratory of Management and Control for Complex Systems (20140102) and the Excellent Young and Middle-Aged Scientist Award Grant of Shandong Province of China (BS2013 DX043)
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Li, C., Ren, W., Wang, H., Yi, J. (2015). Sensor Data Driven Modeling and Control of Personalized Thermal Comfort Using Interval Type-2 Fuzzy Sets. In: Huang, DS., Han, K. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2015. Lecture Notes in Computer Science(), vol 9227. Springer, Cham. https://doi.org/10.1007/978-3-319-22053-6_19
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DOI: https://doi.org/10.1007/978-3-319-22053-6_19
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